Researchers at the Allen Institute and the University of Washington's Paul Allen School of Computer Science and Engineering created a natural language processing algorithm that generates, as well as detects, fake articles.

Researchers at the Allen Institute and the University of Washington's Paul Allen School of Computer Science and Engineering have modified a neural network to create a natural language processing algorithm that generates, as well as detects, convincing fake articles.

The researchers tweaked OpenAI's popular GPT-2 network to produce the "Grover" program, which serves as both a fake-news "generator," and a "discriminator" to identify that false content.

Grover produces disinformation after being fed a massive volume of curated human-written online news texts, supporting a language model that the network utilizes to create its own texts.

The discriminator can identify Grover's fake text because it knows the generator's word-assembling "decoder" component chooses the most likely word combinations in a specific pattern.

The researchers said innovations like Grover offer an "exciting opportunity for defense against neural fake news," as "[t]he best models for generating neural disinformation are also the best models at detecting it."